Artificial intelligence methods and systems for analyzing imagery

ABSTRACT

An artificial intelligence system for analyzing imagery, the system comprising a computing device, the computing device designed and configured to receive a plurality of photographs related to a human subject; analyze the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generate a classification algorithm utilizing the conditional indicator, wherein the classification algorithm utilizes the conditional indicator as an input and outputs a conditional profile; and determine a conditional status of the human subject utilizing the conditional profile.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed toartificial intelligence methods and systems for analyzing imagery.

BACKGROUND

Conditions can be hidden and remain undetected for years on end. Thiscan be quite challenging for individuals, who cannot appropriately seektreatment and manage these hidden problems. This can be furtherfrustrated by an inability to intervene and reverse disease early.

SUMMARY OF THE DISCLOSURE

In an aspect, an artificial intelligence system for analyzing imagery,the system comprising a computing device, the computing device designedand configured to receive a plurality of photographs related to a humansubject; analyze the plurality of photographs to identify a conditionalindicator contained within the plurality of photographs; generate aclassification algorithm utilizing the conditional indicator, whereinthe classification algorithm utilizes the conditional indicator as aninput and outputs a conditional profile; and determine a conditionalstatus of the human subject utilizing the conditional profile.

In an aspect, an artificial intelligence method of analyzing imagery,the method comprising receiving by a computing device, a plurality ofphotographs related to a human subject; analyzing by the computingdevice, the plurality of photographs to identify a conditional indicatorcontained within the plurality of photographs; generating by thecomputing device, a classification algorithm utilizing the conditionalindicator, wherein the classification algorithm utilizes the conditionalindicator as an input and outputs a conditional profile; and determiningby the computing device, a conditional status of the human subjectutilizing the conditional profile.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of anartificial intelligence system for analyzing imagery;

FIG. 2 is a block diagram illustrating an exemplary embodiment of anexpert database;

FIG. 3 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 4 is a diagrammatic representation of aspects of determining aconditional status;

FIG. 5 is a process flow diagram illustrating an exemplary embodiment ofan artificial intelligence method of analyzing imagery; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toartificial intelligence systems and methods for analyzing imagery. In anembodiment, a computing device analyzes a plurality of photographs toidentify conditional profiles of users. Conditional profiles aregenerated using a classification algorithm, and additionalmachine-learning processes.

Referring now to FIG. 1, an exemplary embodiment of an artificialintelligence system 100 for analyzing imagery is illustrated. System 100includes a computing device 104. Computing device 104 may include anycomputing device 104 as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device 104 may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. Computingdevice 104 may include a single computing device 104 operatingindependently or may include two or more computing device 104 operatingin concert, in parallel, sequentially or the like; two or more computingdevices 104 may be included together in a single computing device 104 orin two or more computing devices 104. Computing device 104 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 104 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computing devices104, and any combinations thereof. A network may employ a wired and/or awireless mode of communication. In general, any network topology may beused. Information (e.g., data, software etc.) may be communicated toand/or from a computer and/or a computing device 104. Computing device104 may include but is not limited to, for example, a computing device104 or cluster of computing devices 104 in a first location and a secondcomputing device 104 or cluster of computing devices 104 in a secondlocation. Computing device 104 may include one or more computing devices104 dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device 104 may distribute one ormore computing tasks as described below across a plurality of computingdevices 104 of computing device 104, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices 104. Computing device 104 maybe implemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device 104.

Continuing to refer to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1, computing device 104 is configuredto receive a plurality of photographs 108 relating to a human subject. A“photograph,” as used in this disclosure, is an image created by lightfalling on a photosensitive surface. A photosensitive surface mayinclude photograph film, an electronic image sensor such as acharge-coupled device (CCD), and/or an electronic image sensor such as acomplementary metal oxide semiconductor (CMOS) chip. A photograph may becreated using a camera. A “camera,” as used in this disclosure, is anoptical instrument used to record images. A camera may include forexample, a single-lens reflex (SLR) camera, a large format camera, amedium format camera, a compact camera, a rangefinder camera, a motionpicture camera, a digital camera, and the like.

With continued reference to FIG. 1, computing device 104 is configuredto receive at an image capture device 112 located on computing device104, a wireless transmission from a remote device containing a pluralityof photographs 108 related to a human subject. An “image capturedevice,” as used in this disclosure, is any device suitable to take aphotograph and/or video of a human subject. Image capture device 112 mayinclude for example, a camera, a mobile phone camera, a scanner, and thelike. A “human subject,” as used in this disclosure, includes any userof system 100. A remote device 116, may include without limitation, adisplay in communication with computing device 104, where a display mayinclude any display as described herein. Remote device 116 may includean additional computing device, such as a mobile device, laptop,desktop, computer and the like. In an embodiment, image capture device112 may be located on a remote device 116 operated by a human subject,such as a camera located on a remote device 116 such as a mobile phoneor laptop. In such an instance, a user may take a plurality ofphotographs 108 utilizing a remote device 116. In an embodiment, athird-party, such as a family member, friend, spouse, co-worker, and/oracquittance of the user may take one or more photographs of the userutilizing a remote device 116. For example, a user's boyfriend may takea series of photographs of the user utilizing an image capture device112 such as a camera located on the user's mobile phone. Image capturedevice located on computing device 104 may receive a wirelesstransmission from a remote device 116 containing a plurality ofphotographs 108 related to a human subject utilizing any networkmethodology as described herein.

With continued reference to FIG. 1, a photograph may be related to ahuman subject when the photograph contains an image of the humansubject. For example, a photograph may contain an image of a user'sentire body, or an image of certain parts of a user's body such as animage of the user from the head and up. A photograph may be related to ahuman subject when the photograph was taken by the human subject. Forexample, a photograph taken by a user showing the inside of the user'skitchen is related to the human subject. In yet another non-limitingexample, a photograph taken by a user showing the several of user'sfriends is related to the human subject. A photograph may be related toa human subject when the photograph contains an image showing anyproperty owned and/or operated by the human subject. For example, aphotograph may be related to a user when the photograph shows an imageof a house or dwelling that the user lives in. In yet anothernon-limiting example, a photograph may be related to a human subjectwhen the photograph shows A photograph may be related to a human subjectwhen the photograph contains an image showing any food, nutrition,and/or supplements intended to be consumed and/or ingested by a humansubject. For instance and without limitation, a photograph may containan image of a meal a human subject cooked at home and intends toconsume. In yet another non-limiting example, a photograph may containan image of a meal that a user ordered from a restaurant. A photographmay be related to a human subject when the photograph contains an imageshowing a social event and/or social activity that a user participatesin. For instance and without limitation, a photograph may contain animage showing a user participating in a group fitness class or partakingin a hobby such as knitting. In yet another non-limiting example, aphotograph may contain an image of one or more materials that a user mayuse to participate in a hobby such as a photograph containing an imageof a bike that the user utilizes to go on bike rides.

With continued reference to FIG. 1, computing device 104 is configuredto receive a plurality of photographs 108 related to the human subjectfrom a social networking platform. A “social networking platform,” asused in this disclosure, is any website and/or application that enablesa user to create and share content, and/or to participate in socialnetworking. A social networking platform may include computer-mediatedtechnologies that facilitate the creation or sharing of information,ideas, career interests and other forms of expression through virtualcommunities and networks. A social networking platform may containuser-generated content such as text posts, comments, photographs,videos, and/or data generated through online interactions. A socialnetworking platform may allow a user to create an individual profilethat identifies background demographic information about the user, suchas for example the user's name, neighborhood where the user lives,highest education that the user has achieved, work and/or employmenthistory, marital status, reviews of the user by third-parties such asfriends, colleagues, neighbors and the like. For instance and withoutlimitation, a social networking platform may include an application suchas but not limited to, FACEBOOK, INC. of Menlo Park, Calif.; YOUTUBE ofSan Bruno, Calif.; WHATSAPP of Menlo Park, Calif.; INSTAGRAM of MenloPark, Calif.; TIKTOK of Shanghai, China; TWITTER of San Francisco,Calif.; LINKEDIN of Sunnyvale, Calif.; SNAPCHAT of Santa Monica, Calif.;PINTEREST of San Francisco, Calif. and the like. Computing device 104may receive a plurality of photographs 108 from a social networkingplatform utilizing any network methodology and/or network transmissionas described herein. In an embodiment, computing device 104 may extracta plurality of photographs 108 pertaining to a user from one or moresocial networking platforms utilizing any data scraping techniques. Datascraping may include extracting data from human-readable output comingfrom another program. In an embodiment, computing device 104 may scrapedata from one or more websites, utilizing a web scraper such as anapplication programming interface (API). An API includes any computinginterface to a software component or a system that defines how othercomponents and/or other systems can use it. An API may define differentkinds calls or requests that can be made, how to make them, data formatsthat should be used, conventions to follow and the like. An API may alsoinclude extension mechanisms so that users can extend existingfunctionality in various ways and to varying degrees. An API may becustomized, and/or designed based on an industry standard to ensureinteroperability. In an embodiment, an API may allow for the combinationof multiple APIs into a new application known as mashups, which mayfacilitate the sharing of content and data between communities andapplications. A web scraper may contain data feeds from web servers suchas JavaScript Object Notation (JSON) that may be used as a transportstorage mechanism between a computing device 104 and a webserver. A webscraper may utilize one or more techniques in document object model(DOM) parsing, computer vision, and/or natural language processing tosimulate human processing that occurs when viewing a webpage to extractuseful and/or meaningful information. In an embodiment, computing device104 may receive a plurality of photographs 108 related to a humansubject from a social networking platform based on one or morepermissions controlled by the human subject. For instance and withoutlimitation, the human subject may allow computing device 104 to retrievephotographs from a first social networking platform but not a secondsocial networking platform. In yet another non-limiting example, a usermay allow computing device 104 to retrieve photographs from a firstsocial networking platform between a certain time period, such asbetween May through August during a certain year, or only during aspecific year such as during the year 2019.

With continued reference to FIG. 1, computing device 104 is configuredto analyze a plurality of photographs 108 to identify a conditionalindicator 120 contained within the plurality of photographs 108. A“conditional indicator,” as used in this disclosure, is a determinant ofa user's health; conditional indicator may be any determinant of theuser's health. A determinant of health, as used herein, is a factor thatimpacts a person's health and wellness; determinant may include anyfactor that can have an impact on one's health and wellness. Adeterminant of health may include factors such as where a user lives,the state of a user's home environment, genetics, income, educationlevel, social relationships with family, friends, acquaintances and thelike, race, gender, age, nutrition, social status community involvementand/or engagement, major life events, physical activity levels, smokingstatus, alcohol and drug use, access to healthcare, health behaviors,and the like. For instance and without limitation, a conditionalindicator 120 may identify one or more determinants of health containedwithin an image, such as a photograph that contains an image of a usersmoking cigarettes and drinking alcohol. In yet another non-limitingexample, a conditional indicator 120 may reveal if a user is surroundedby other people in any photographs, such as if the user is pictured in acircle of friends or if they are routinely pictured being alone. Aconditional indicator 120 may identify any nutritional behaviors and/oreating patterns of a user. For example, a conditional indicator 120 mayidentify different types of food, and/or nutrients that a user consumes,such as a plurality of photographs 108 that show that the userfrequently eats meals from fast food restaurants that contain very fewif any vegetables. A conditional indicator 120 may identify nutritionalbehaviors such as if a user routinely cooks meals at home, orders foodto go from restaurants, and/or eats meals at restaurants. A conditionalindicator 120 may indicate one or more social habits and/or factorspertaining to a user, such as if a user is a member of a church orreligious organization, if a user participates in social activities withfriends and the like. A conditional indicator 120 may indicate one ormore fitness habits of a user, such as if a user is pictured engaging inphysical activity such as by running or lifting weights. A conditionalindicator 120 may identify one or more social determinants of a user'shealth, such as the user's age, race, and/or gender. A conditionalindicator 120 may identify one or more behavior characteristics of auser, such as any photographs that contain an image of the user mayreflect if the user is smiling or posing happily and for the camera,which may indicate that the person is extroverted and sociallyconnected, while an image of the user who is hiding from the camera mayindicate that the user is shy and introverted. A conditional indicatormay indicate one or more internal determinants of a user's health. Forexample, a conditional indicator that reflects a user who frequentlywithin a plurality of photographs 108 has pale skin and bags under theuser's eyes may be suffering from a medical condition such as fatigueand/or anemia. In yet another non-limiting example, a user with cracksin corner of the user's lips may be suffering from a Vitamin Bdeficiency.

With continued reference to FIG. 1, computing device 104 may identifyone or more conditional indicator 120 contained within a plurality ofphotographs 108 based on expert input. One or more experts may provideinput that may be stored within expert database 124. Expert database 124may include any data structure for ordered storage and retrieval ofdata, which may be implemented as a hardware or software module. Anexpert database 124 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure.

With continued reference to FIG. 1, computing device 104 is configuredto identify a conditional indicator group 128 identified within aplurality of photographs 108. A “conditional indicator group,” as usedin this disclosure, is any determinant of health that has a sharedcommonality and/or any determinant of health that is repeatedlycontained within the plurality of photographs. A determinant of healthmay have a shared commonality when the determinant of health may have acommon root cause, or when two or more conditional indicator 120 maycontribute to the same determinant of health. For instance and withoutlimitation, a first conditional indicator 120 such as alcohol use, and asecond conditional indicator 120 such as marital status may both relateto a conditional indicator group 128 such as social determinants ofhealth. In yet another non-limiting example, a first conditionalindicator 120 such as cooking habits and a second conditional indicator120 such as recent meals consumed by a user, and a third conditionindicator such as supplements consumed by a user, may all relate to aconditional indicator group 128 of nutritional determinants of health. Aconditional indicator group may contain a determinant of health that isrepeatedly contained within the plurality of photographs, such asfitness activities that a user engages in may be repeatedly containedwithin a plurality of photographs, and as such, a conditional indicatorgroup may identify the repeated photographs containing fitnessactivities as belonging to a conditional indicator group 128 of exercisedeterminants of health. In yet another non-limiting example, aconditional indicator group may contain a determinant of health that isrepeatedly contained within the plurality of photographs, such aspictures of meals that a user consumes as belonging to a conditionalindicator group 128 of nutritional determinants of health. Computingdevice 104 may identify one or more conditional indicator group 128utilizing input contained within expert database 124. Computing device104 generates a label identifying a conditional indicator group 128. A“label,” as used in this disclosure, is data, including any numerical,symbolic, and/or character data indicating the group that a conditionalindicator 120 belongs to. In an embodiment, a conditional indicator maybelong to one or more groups. For instance and without limitation, aconditional indicator 120 such as meal patterns may belong to a firstconditional indicator group 128 such as nutritional determinants ofhealth and a second conditional indicator group 128 such as socialdeterminants of health. In yet another non-limiting example, aconditional indicator 120 such as exercise habits may belong to a firstconditional indicator 120 such as health behavior determinants ofhealth, and a second conditional indicator 120 such as physicaldeterminants of health.

With continued reference to FIG. 1, computing device 104 is configuredto identify information missing from an identified conditional indicatorgroup. Information may be missing when there is not any informationpertaining to a conditional indicator group 128, and/or when there maynot be enough information gathered pertaining to a conditional indicatorgroup 128. For instance and without limitation, computing device 104 maydetermine that a conditional indicator group 128 containing informationabout a user's nutritional habits does not contain enough informationbecause there are very few photographs contained within the plurality ofphotographs 108 that have information pertaining to the user'snutritional habits. Computing device 104 is configured to transmit arequest to a remote device 116 operated by the human subject, to obtainmore information. Computing device 104 transmits the request to a remotedevice 116 operated by the human subject utilizing any networkmethodology as described herein. A request to obtain more informationmay include a series of one or more questions and/or comments for a userto elaborate on. In an embodiment, a request to obtain more informationmay include a questionnaire, that may contain user responses toquestions. In an embodiment, a request to obtain more information may begenerated based on one or more expert inputs contained within expertdatabase 124. Computing device 104 receives from the remote device 116operated by the human subject a response containing at least an elementof information. An “element of information,” as used in this disclosure,includes any information that is not possessed by computing device 104.

With continued reference to FIG. 1, computing device 104 is configuredto generate a classification algorithm 132 utilizing a conditionalindicator 120. A “classification algorithm,” as used in this disclosure,is a process whereby a computing device 104 derives, from training data,a model for sorting inputs into categories or bins of data. Trainingdata,” as used in this disclosure, is data containing correlations thata machine-learning process 152 may use to model relationships betweentwo or more categories of data elements. For instance, and withoutlimitation, training data may include a plurality of data entries, eachentry representing a set of data elements that were recorded, received,and/or generated together; data elements may be correlated by sharedexistence in a given data entry, by proximity in a given data entry, orthe like. Multiple data entries in training data may evince one or moretrends in correlations between categories of data elements; forinstance, and without limitation, a higher value of a first data elementbelonging to a first category of data element may tend to correlate to ahigher value of a second data element belonging to a second category ofdata element, indicating a possible proportional or other mathematicalrelationship linking values belonging to the two categories. Multiplecategories of data elements may be related in training data according tovarious correlations; correlations may indicate causative and/orpredictive links between categories of data elements, which may bemodeled as relationships such as mathematical relationships bymachine-learning process 152 es as described in further detail below.Training data may be formatted and/or organized by categories of dataelements, for instance by associating data elements with one or moredescriptors corresponding to categories of data elements. As anon-limiting example, training data may include data entered instandardized forms by persons or processes, such that entry of a givendata element in a given field in a form may be mapped to one or moredescriptors of categories. Elements in training data may be linked todescriptors of categories by tags, tokens, or other data elements; forinstance, and without limitation, training data may be provided infixed-length formats, formats linking positions of data to categoriessuch as comma-separated value (CSV) formats and/or self-describingformats such as extensible markup language (XML), enabling processes ordevices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by computing device 104 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

With continued reference to FIG. 1, a “classification algorithm,” asused in this disclosure, is a process whereby a computing device 104derives, from training data, a model for sorting inputs into categoriesor bins of data. Training data includes any of the training data asdescribed herein. Classification may be performed using, withoutlimitation, linear classifiers such as without limitation logisticregression and/or naive Bayes classifiers, nearest neighbor classifiersincluding without limitation k-nearest neighbors classifiers, supportvector machines, decision trees, boosted trees, random forestclassifiers, and/or neural network-based classifiers.

With continued reference to FIG. 1, classification algorithm 132 mayinclude generating a Naïve Bayes classification algorithm 132. NaïveBayes classification algorithm 132 generates classifiers by assigningclass labels to problem instances, represented as vectors of featurevalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm 132 may include generating a family ofalgorithms that assume that the value of a particular feature isindependent of the value of any other feature, given a class variable.Naïve Bayes classification algorithm 132 may be based on Bayes Theoremexpressed as P(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability ofhypothesis A given data B also known as posterior probability; P(B/A) isthe probability of data B given that the hypothesis A was true; P(A) isthe probability of hypothesis A being true regardless of data also knownas prior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming classification training data into a frequencytable. Computing device 104 may then calculate a likelihood table bycalculating probabilities of different data entries and classificationlabels. Computing device 104 utilizes a naïve Bayes equation tocalculate a posterior probability for each class. A class containing thehighest posterior probability is the outcome of prediction. Naïve Bayesclassification algorithm 132 may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm 132 mayinclude a multinomial model that is used for discrete counts. NaïveBayes classification algorithm 132 may include a Bernoulli model thatmay be utilized when feature vectors are binary. Naïve Bayesclassification algorithm 132 utilizes training data and at least aretrieved element of user data as an input to output a user metabolicstate. A metabolic state may be identified utilizing a classificationlabel, where a “classification label” as used in this disclosure,includes a label that indicates whether an input belongs to a particularclass or not. In an embodiment, a classification label may include anindication as to the metabolic state of the user. For example, a userwith hyperthyroidism who is a hyper-metabolizer may be classified to ametabolic state that indicates that the user is a hypermetabolizer,whereas a user who is not active, and does not engage in physicalactivity may be classified to a metabolic state that indicates that theuser is a slow metabolizer.

With continued reference to FIG. 1, classification algorithm 132 mayinclude generating a K-nearest neighbor (KNN) algorithm. A “K-nearestneighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

With continued reference to FIG. 1, classification algorithm 132utilizes a conditional indicator 120 as an input and outputs aconditional profile 136. A “conditional profile,” as used in thisdisclosure, is data including any numerical, character, and/or symbolicdata describing the overall health and/or well-being of a human subject.A conditional profile 136 may include information describing one or moresuspected conditions that a user may be suffering from. A “condition,”as used in this disclosure, is the identification of any state of auser's health. A condition may identify a likelihood or percentage thata user suffers from a specific illness such as a user who has adepressed mood, low energy, and fatigue may have a high likelihood ofsuffering from an illness of depression. A condition may identify thelikelihood of a user suffering from a pre-condition, such aspre-diabetes. A condition may identify a likelihood that a user willdevelop a disease such as the likelihood that a user will develop heartdisease or breast cancer. A condition may identify a health status thatmay reflect one or more indicators of health. For instance and withoutlimitation, a classification algorithm 132 may utilize a conditionalindicator 120 such as pale names to classify the user to a conditionalprofile 136 that reflects the likelihood that a user has anemia. In yetanother non-limiting example, a classification algorithm 132 may utilizea conditional indicator 120 such as breakouts on chin/jawline toclassify the user to a conditional profile 136 that reflects thelikelihood that a user has a hormone disruption.

With continued reference to FIG. 1, computing device 104 is configuredto retrieve an element of user physiological data 140. As used in thisdisclosure, “physiological data” is any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. For instance, and without limitation,a particular set of biomarkers, test results, and/or biochemicalinformation may be recognized in a given medical field as useful foridentifying various disease conditions or prognoses within a relevantfield. As a non-limiting example, and without limitation, physiologicaldata describing red blood cells, such as red blood cell count,hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1, physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1, physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices 104; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing moduleas described in this disclosure. As a non-limiting example, biologicalextraction may include a psychological profile; the psychologicalprofile may be obtained utilizing a questionnaire performed by the user.

Still referring to FIG. 1, physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences or other genetic sequencescontained in one or more chromosomes in human cells. Genomic data mayinclude, without limitation, ribonucleic acid (RNA) samples and/orsequences, such as samples and/or sequences of messenger RNA (mRNA) orthe like taken from human cells. Genetic data may include telomerelengths. Genomic data may include epigenetic data including datadescribing one or more states of methylation of genetic material.Physiological state data may include proteomic data, which as usedherein is data describing all proteins produced and/or modified by anorganism, colony of organisms, or system of organisms, and/or a subsetthereof. Physiological state data may include data concerning amicrobiome of a person, which as used herein includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data of a person, as described in further detailbelow.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1, physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a server may present to user aset of assessment questions designed or intended to evaluate a currentstate of mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; at least a server mayprovide user-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1, assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device 116; third-party device 116 may include, withoutlimitation, a server or other device (not shown) that performs automatedcognitive, psychological, behavioral, personality, or other assessments.Third-party device 116 may include a device operated by an informedadvisor. An informed advisor may include any medical professional whomay assist and/or participate in the medical treatment of a user. Aninformed advisor may include a medical doctor, nurse, physicianassistant, pharmacist, yoga instructor, nutritionist, spiritual healer,meditation teacher, fitness coach, health coach, life coach, and thelike.

With continued reference to FIG. 1, physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1, physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1, user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1, epigenetic, as used herein, includesany user body measurements describing changes to a genome that do notinvolve corresponding changes in nucleotide sequence. Epigenetic bodymeasurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1, gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1, gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1, gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,Campylobacter species, Clostridium difficile, Cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1, microbiome, as used herein, includesecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease-causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen-based breath tests, fructose-basedbreath tests, Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1, nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1, nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1, genetic as used herein, includes anyinherited trait. Inherited traits may include genetic material containedwith DNA including for example, nucleotides. Nucleotides include adenine(A), cytosine (C), guanine (G), and thymine (T). Genetic information maybe contained within the specific sequence of an individual's nucleotidesand sequence throughout a gene or DNA chain. Genetics may include how aparticular genetic sequence may contribute to a tendency to develop acertain disease such as cancer or Alzheimer's disease.

With continued reference to FIG. 1, genetic body measurement may includeone or more results from one or more blood tests, hair tests, skintests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1, genetic body measurement may includean analysis of COMT gene that is responsible for producing enzymes thatmetabolize neurotransmitters. Genetic body measurement may include ananalysis of DRD2 gene that produces dopamine receptors in the brain.Genetic body measurement may include an analysis of ADRA2B gene thatproduces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vasodilation and vasoconstriction of blood vessels.

With continued reference to FIG. 1, genetic body measurement may includeACE gene that is involved in producing enzymes that regulate bloodpressure. Genetic body measurement may include SLCO1B1 gene that directspharmaceutical compounds such as statins into cells. Genetic bodymeasurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fullness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1, genetic body measurement may includeCYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1, metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1, physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MM) equipment, ultrasound equipment,optical scanning equipment such as photo-plethysmographic equipment, orthe like. A sensor may include any electromagnetic sensor, includingwithout limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100. User data may includea profile, such as a psychological profile, generated using previousitem selections by the user; profile may include, without limitation, aset of actions and/or navigational actions performed as described infurther detail below, which may be combined with biological extractiondata and/or other user data for processes such as classification to usersets as described in further detail below.

Still referring to FIG. 1, retrieval of biological extraction mayinclude, without limitation, reception of biological extraction fromanother computing device 104 such as a device operated by a medicaland/or diagnostic professional and/or entity, a user client device,and/or any device suitable for use as a third-party device as describedin further detail below. Biological extraction may be received via aquestionnaire posted and/or displayed on a third-party device asdescribed below, inputs to which may be processed as described infurther detail below. Alternatively or additionally, biologicalextraction may be stored in and/or retrieved from a user database 144.User database 144 may include any data structure for ordered storage andretrieval of data, which may be implemented as a hardware or softwaremodule. A user database 144 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A user database 144 may include a pluralityof data entries and/or records corresponding to user tests as describedabove. Data entries in a user database 144 may be flagged with or linkedto one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data entries in a user database 144 mayreflect categories, cohorts, and/or populations of data consistentlywith this disclosure. User database 144 may be located in memory ofcomputing device 104 and/or on another device in and/or in communicationwith system 100.

With continued reference to FIG. 1, and as noted above, retrieval ofbiological extract may be performed multiple sequential and/orconcurrent times, and any process using biological extract as describedbelow may be performed multiple sequential and/or concurrent times;likewise, biological extract may include multiple elements ofphysiological data, which may be used in combination for anydetermination and/or other processes as described below.

An element of physiological data may include a user reported element ofphysiological data. A user reported element of physiological data mayinclude any medical data pertaining to a user, supplied by a user. Forexample, a user reported element of physiological data may include anyprevious health history, health records, diagnosis, medications,treatments, major surgeries, complications, and the like that the usermay be suffering from. For example, a user reported an element ofphysiological data may include an anaphylactic reaction to all tree nutsthat the user was diagnosed with as a young child. In yet anothernon-limiting example, a user reported element of physiological data maydescribe a previous diagnosis such as endometriosis that the user wasdiagnosed with three years back, and treatments that the user engages into manage her endometriosis, including supplementation with fish oil andfollowing a gluten free diet. In yet another non-limiting example, auser may provide one or more elements of health history information,such as when a user may select how much of a user's medical records theuser seeks to share with computing device 104. For example, a user mayprefer to share only the user's hospitalization records and not theuser's current medication list. In yet another non-limiting example, auser may seek to share as many records as are available for the user,such as the user's entire vaccination history. In yet anothernon-limiting example, a user may share health history information thatis available to the user, such as when records may become lost ormisplaced. An element of physiological data may include an amount ofinformation or certain records based on a user's entire medical recordthat the user seeks to share and allow system 100 and/or a computingdevice 104 to have access to. For example, a user may prefer to shareonly the user's hospitalization records and not the user's currentmedication list. In yet another non-limiting example, a user may seek toshare as many records as are available for the user, such as the user'sentire health history. In yet another non-limiting example, a user maynot wish to share any information pertaining to a user's health history.In yet another non-limiting example, a user may be unable to share anyinformation pertaining to a user's health history, because the user maybe adopted and may not have access to health records, or the user isunable to locate any health records for the user and the like. Anelement of physiological data may include a user reportedself-assessment. A “self-assessment” as used in this disclosure, is anyquestionnaire that may prompt and/or ask a user for any element of userhealth history. For instance and without limitation, a self-assessmentmay seek to obtain information including demographic information such asa user's full legal name, sex, date of birth, marital status, date oflast physical exam and the like. A self-assessment may seek to obtaininformation regarding a user's childhood illness such as if the usersuffered from measles, mumps, rubella, chickenpox, rheumatic fever,polio and the like. A self-assessment may seek to obtain any vaccinationinformation and dates a user received vaccinations such as tetanus,hepatitis, influenza, pneumonia, chickenpox, measles mumps and rubella(MMR), and the like. A self-assessment may seek to obtain any medicalproblems that other doctors and/or medical professionals may havediagnosed. A self-assessment may seek to obtain any information aboutsurgeries or hospitalizations the user experienced. A self-assessmentmay seek to obtain information about previously prescribed drugs,over-the-counter drugs, supplements, vitamins, and/or inhalers the userwas prescribed. A self-assessment may seek to obtain informationregarding a user's health habits such as exercise preferences, nutritionand diet that a user follows, caffeine consumption, alcohol consumption,tobacco use, recreational drug use, sexual health, personal safety,family health history, mental health, other problems, other remarks,information pertaining to women only, information pertaining to men onlyand the like. Computing device 104 is configured to generate theclassification algorithm 132 utilizing the element of user physiologicaldata 140.

With continued reference to FIG. 1, computing device 104 is configuredto determine a conditional status 148 of a human subject utilizing aconditional profile 136. A “conditional status” as used in thisdisclosure, is the identification of any health conditions that a usermay be and/or is likely to be suffering from. A conditional status 148may identify a disease likelihood score, defined for the purposes ofthis disclosure as a quantitative datum that indicates the likelihoodthat a user has a disease. A likelihood may include a numericallikelihood reported on a scale, and/or may include a likelihood reportedbased on character values indicating how probable or likely it is that auser has a disease. For instance and without limitation, a conditionalstatus 148 may indicate that a user who has pale lips, a swollen face,and red eyes may have a high likelihood of suffering from a disease suchas a common cold. In yet another non-limiting example, a conditionalstatus 148 may indicate that a user who has a very small social networkconsisting of a few friends, consumes alcohol, and does not exercise hasa moderate likelihood of having depression. A conditional status 148includes a treatment identifier. A “treatment identifier,” as used inthis disclosure, is an element of data identifying a therapeutic agentand/or remedy, that may correct or lessen a condition identified withina conditional status 148. A treatment identifier may include treatmentsthat include prescription medications, over the counter medications,vitamins, supplements, herbals, nutritional interventions, fitnessprograms, meditation sequences, yoga classes and the like. For instanceand without limitation, computing device 104 may generate for a userwith a condition such as the common cold, a treatment identifier thatincludes chicken and rice soup, along with elderberry supplement andplenty of fluids. In yet another non-limiting example, a treatmentidentifier may recommend excess consumption of cruciferous vegetablesfor a user with a condition such as pre-menstrual syndrome (PMS).

With continued reference to FIG. 1, computing device 104 determines theconditional status 148 of a human subject utilizing a machine-learningprocess 152. A “machine learning process” is a process that automatedlyuses a body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computing device 104and/or module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

With continued reference to FIG. 1, computing device 104 may be designedand configured to create a machine-learning model using techniques fordevelopment of linear regression models. Linear regression models mayinclude ordinary least squares regression, which aims to minimize thesquare of the difference between predicted outcomes and actual outcomesaccording to an appropriate norm for measuring such a difference (e.g. avector-space distance norm); coefficients of the resulting linearequation may be modified to improve minimization. Linear regressionmodels may include ridge regression methods, where the function to beminimized includes the least-squares function plus term multiplying thesquare of each coefficient by a scalar amount to penalize largecoefficients. Linear regression models may include least absoluteshrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 1, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 1, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data.

Continuing to refer to FIG. 1, machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayutilize a conditional profile 136 as described above as inputs, aconditional status 148 as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various possible variations ofsupervised machine learning algorithms that may be used to determinerelation between inputs and outputs. Supervised machine-learning process152 es may include classification algorithm 132 as defined above.

Still referring to FIG. 1, machine learning process es may includeunsupervised processes. An unsupervised machine-learning process 152, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning process152 may be free to discover any structure, relationship, and/orcorrelation provided in the data. Unsupervised processes may not requirea response variable; unsupervised processes may be used to findinteresting patterns and/or inferences between variables, to determine adegree of correlation between two or more variables, or the like.

With continued reference to FIG. 1, machine-learning process 152 asdescribed in this disclosure may be used to generate machine-learningmodels. A machine-learning model, as used herein, is a mathematicalrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process 152 including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning process152 es to calculate an output datum. As a further non-limiting example,a machine-learning model may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 1, at least a machine-learning process 152 mayinclude a lazy-learning process and/or protocol, which may alternativelybe referred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship. As a non-limiting example, an initialheuristic may include a ranking of associations between inputs andelements of training data. Heuristic may include selecting some numberof highest-ranking associations and/or training data elements. Lazylearning may implement any suitable lazy learning algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.Computing device 104 calculates a machine-learning process 152 whereinthe machine-learning process 152 utilizes a conditional profile 136 asan input and outputs a conditional status. Computing device 104determines the conditional status 148 of a human subject utilizing themachine-learning process.

With continued reference to FIG. 1, computing device 104 is configuredto transmit the conditional status 148 of a human subject to a remotedevice 116 operated by an informed advisor. An “informed advisor,” asused in this disclosure, includes any individual who may be involved incontributing to the health and well-being of the human subject. Aninformed advisor may include a physician, doctor, nurse, physicianassistant, nurse practitioner, pharmacist, psychiatrist, psychologist,nutritionist, dietician, yoga instructor, meditation teacher, spiritualadvisor, church leader, and the like. Computing device 104 receives aninput generated by an informed advisor in response to the conditionalstatus 148 of the human subject. In an embodiment, an input generated byan informed advisor may provide background information, confirminformation about a user, update information about a user, and/orprovide feedback regarding a conditional status. For example, an inputgenerated by a user's nutritional advisor may confirm a diseaselikelihood contained within a conditional status 148 and indicate that auser does have a Vitamin D deficiency. In yet another non-limitingexample, an input generated by an informed advisor may confirm that atreatment identifier contained within a conditional status 148 isappropriate for a user. Computing device 104 updates a conditionalstatus 148 utilizing an input generated by an informed advisor. Updatinga conditional status 148 may include updating a disease likelihood scoreso as to confirm the likelihood of a user disease.

Referring now to FIG. 2, an exemplary embodiment 200 of expert database124 is illustrated. Expert database 124 may, as a non-limiting example,organize data stored in the expert database 124 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of expert database 124 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 2, one or more database tables in expertdatabase 124 may include, as a non-limiting example, an expertconditional indicator table 204; expert conditional indicator 204 mayinclude expert information relating to conditional indicator 120. One ormore database tables in expert database 124 may include, as anon-limiting example, an expert information table 208; expertinformation table 208 may include expert information relating to anyinformation necessary within system 100, including for example,information relating to conditional indicators. One or more databasetables in expert database 124 may include, expert conditional profile212; expert conditional profile table 212 may include expert informationrelating to conditional profile 136. One or more database tables inexpert database 124 may include, expert photograph table 216; expertphotograph table 216 may include expert information relating tophotographs. One or more database tables in expert database 124 mayinclude, expert treatment table 220; expert treatment table 220 mayinclude expert information relating to treatments. One or more databasetables in expert database 124 may include expert disease table 224;expert disease table 224 may include expert information relating todiseases.

In an embodiment, and still referring to FIG. 2, a forms processingmodule 228 may sort data entered in a submission via a graphical userinterface 232 receiving expert submissions by, for instance, sortingdata from entries in the graphical user interface 232 to relatedcategories of data; for instance, data entered in an entry relating inthe graphical user interface 232 to a conditional indicator 120 such associal determinants of health, which may be provided to expertconditional indicator 120 table 204, while data entered in an entryrelating to recommended treatments for acne vulgaris, which may beprovided to expert disease table 224. Where data is chosen by an expertfrom pre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, a language processingmodule may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map data to existing labels and/orcategories. Similarly, data from an expert textual submission 236, suchas accomplished by filling out a paper or PDF form and/or submittingnarrative information, may likewise be processed using languageprocessing module.

Still referring to FIG. 2, a language processing module 240 may includeany hardware and/or software module. Language processing module 240 maybe configured to extract, from the one or more documents, one or morewords. One or more words may include, without limitation, strings of oneor characters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Textual data may beparsed into tokens, which may include a simple word sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains” forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 2 language processing module 240 may compareextracted words to categories of data to be analyzed; such data forcomparison may be entered on computing device 104 as described aboveusing expert data inputs or the like. In an embodiment, one or morecategories may be enumerated, to find total count of mentions in suchdocuments. Alternatively or additionally, language processing module 240may operate to produce a language processing model. Language processingmodel may include a program automatically generated by at least a serverand/or language processing module 240 to produce associations betweenone or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations between such words and otherelements of data analyzed, processed and/or stored by system 100.Associations between language elements, may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. As a further example, statistical correlations and/ormathematical associations may include probabilistic formulas orrelationships indicating a positive and/or negative association betweenat least an extracted word and/or a given category of data; positive ornegative indication may include an indication that a given document isor is not indicating a category of data.

Still referring to FIG. 2, language processing module 240 and/orcomputing device 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm 132; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 240may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm 132 that returns ranked associations.

Continuing to refer to FIG. 2, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 2, language processing module 240 may use acorpus of documents to generate associations between language elementsin a language processing module 240, and computing device 104 may thenuse such associations to analyze words extracted from one or moredocuments. Documents may be entered into computing device 104 by beinguploaded by an expert or other persons using, without limitation, filetransfer protocol (FTP) or other suitable methods for transmissionand/or upload of documents; alternatively or additionally, where adocument is identified by a citation, a uniform resource identifier(URI), uniform resource locator (URL) or other datum permittingunambiguous identification of the document, computing device 104 mayautomatically obtain the document using such an identifier, for instanceby submitting a request to a database or compendium of documents such asJSTOR as provided by Ithaka Harbors, Inc. of New York.

With continued reference to FIG. 2, data may be extracted from expertpapers 244, which may include without limitation publications in medicaland/or scientific journals, by language processing module 240 via anysuitable process as described herein. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional methods whereby novel terms may be separated fromalready-classified terms and/or synonyms therefore, as consistent withthis disclosure.

Referring now to FIG. 3, an exemplary embodiment 300 of user database144 is illustrated. User database 144 may be implemented as any datastructure suitable for use as expert database 124 as described above inmore detail. One or more tables contained within user database 144 mayinclude user physiological data table 304; user physiological data table304 may include one or more elements of physiological data, one or morebiological extractions, and/or one or more elements of user healthinformation. One or more tables contained within user database 144 mayinclude user photograph table 308; user photograph table 308 may includeone or more photographs of a user. One or more tables contained withinuser database 144 may include social networking platform table 312;social networking platform table 312 may include information pertainingto one or more social networking platforms that a user may engage with.One or more tables contained within user database 144 may includeconditional indicator table 316; conditional indicator table 316 mayinclude information pertaining to one or more conditional indicator 120for a user. One or more tables contained within user database 144 mayinclude user data sharing permissions table 320; user data sharingpermissions table 320 may include information pertaining to data a usermay share with system 100 and/or with social networking platforms. Oneor more tables contained within user database 144 may include userconditional profile table 324; user conditional profile table 324 mayinclude information pertaining to conditional profiles 136 relating to auser.

Referring now to FIG. 4, an exemplary embodiment 400 of generating aconditional status 148 is generated. Computing device 104 receives aplurality of photographs 108, which may be received from a plurality ofsources. For example, photographs may be received from user inputs fromremote device 116. Photographs may be received at an image capturedevice 112 located on computing device 104. Photographs may be receivedfrom one or more social networking platforms as described above in moredetail. Computing device 104 analyzes a plurality of photographs 108 toidentify one or more conditional indicator 120 describes any determinantof a user's health. In an embodiment, computing device 104 may generatea plurality of conditional indicator 120. For instance and withoutlimitation, computing device 104 may examine a plurality of photographs108 pertaining to a human subject and generate a first conditionalindicator 120 “A” that indicates a user drinks alcohol, a secondconditional indicator “B” that indicates the user engages in physicalactivity, and a third conditional indicator 120 “C” that indicates a theuser orders takeout meals from restaurants as opposed to cooking mealsat home. In an embodiment, one or more conditional indicator 120 may begenerated based on input contained within expert database 124. Computingdevice 104 utilizes conditional indicator 120 in combination with aclassification algorithm 132 to generate a conditional profile 136.Classification algorithm 132 includes any of the classificationalgorithm 132 as described above in more detail. Conditional profileincludes any of the conditional profile 136 as described above in moredetail in reference to FIG. 1. Computing device 104 utilizes aconditional profile 136 to determine a conditional status 148 of theuser. A conditional status 148 may indicate the likelihood that a userhas a particular disease. For example, a conditional status 148 mayindicate that a user has a low likelihood of having necrotizingfasciitis, but a high likelihood of having urticaria. A conditionalstatus 148 may contain a treatment identifier, which may identify one ormore treatments available for a disease identified within a diseaselikelihood score. In an embodiment, a conditional status 148 may begenerated based on information contained within expert database 124. Inan embodiment, a conditional status 148 may be generated utilizing oneor more machine-learning process 152 es. A machine-learning process 152includes any of the machine-learning process 152 es as described abovein more detail in reference to FIG. 1.

Referring now to FIG. 5, an exemplary embodiment 500 of an artificialintelligence method of analyzing imagery is illustrated. At step 505, acomputing device 104 receives a plurality of photographs 108 related toa human subject. A photograph, includes any of the photographs asdescribed above in more detail in reference to FIG. 1. Computing device104 receives at an image capture device 112 located on computing device104 a wireless transmission from a remote device 116 containing aplurality of photographs 108 related to the human subject. An imagecapture device 112 includes any of the image capture device 112 asdescribed above in more detail in reference to FIG. 1. Computing device104 receives a wireless transmission from a remote device 116 utilizingany network methodology as described herein. In an embodiment, an imagecapture device 112 may be located on a remote device 116. In such aninstance, a user may capture one or more photographs of the user and/orphotographs related to the user as described above. A user may transmitthe plurality of photographs 108 to computing device 104 utilizing anynetwork methodology as described herein. Computing device 104 receives aplurality of photographs 108 related to a human subject from a socialnetworking platform. A social networking platform includes any of thesocial networking platforms as described above in more detail inreference to FIG. 1. In an embodiment, a user may specify requirementsrelating to a social networking platform, indicating what socialnetworking platforms computing device 104 may receive photographs from,dates of photographs and what dates photographs may be received from.One or more user preferences regarding social networking platforms maybe stored in user database 144 as described above in more detail inreference to FIGS. 1-4. In an embodiment, computing device 104 mayreceive a plurality of photographs 108 from a social networking platformand/or from a website by scraping data utilizing a web scraper, asdescribed above in more detail in reference to FIG. 1.

With continued reference to FIG. 5, at step 510, a computing device 104analyzes a plurality of photographs 108 to identify a conditionalindicator 120 contained within the plurality of photographs 108. Aconditional indicator 120 includes any of the conditional indicator 120as described above in more detail in reference to FIGS. 1-4. Aconditional indicator 120 describes any determinant of health of a user,including any factor that can have an impact on one's health andwellness. A determinant of health may include factors such as where auser lives, the state of a user's home environment, genetics, income,education level, social relationships with family, friends,acquaintances and the like, race, gender, age, nutrition, social statuscommunity involvement and/or engagement, major life events, physicalactivity levels, smoking status, alcohol and drug use, access tohealthcare, health behaviors, and the like. Computing device 104identifies conditional indicator 120 contained within a plurality ofphotographs utilizing input contained within expert database 124. Forinstance and without limitation, computing device 104 may identifyconditional indicators contained within a plurality of photographs 108that indicate the user has a large social network of friends, the userdoes not drink alcohol or smoke, and the user has a butterfly shapedrash across the bridge of the user's nose and cheeks.

With continued reference to FIG. 5, computing device 104 identifies aconditional indicator group 128 identified within a plurality ofphotographs 108. A conditional indicator group 128, includes any of theconditional indicator group 128 as described above in more detail inreference to FIG. 1. A conditional indicator group may contain one ormore shared determinants of health. For example, a conditional indicatorgroup may include socioeconomic determinants of health, behavioraldeterminants of health, environmental determinants of health,physiological determinants of health, genetic determinants of health,epigenetic determinants of health and the like. Computing device 104identifies a conditional indicator group 128 utilizing input containedwithin expert database 124. Computing device 104 generates a labelidentifying a conditional indicator group 128. A label includes any ofthe labels as described above in more detail in reference to FIG. 1.Computing device 104 identifies information missing from an identifiedgroup of a conditional indicator 120. For instance and withoutlimitation, a conditional indicator group 128 such as behavioraldeterminants of health may only contain information that identifies theuser as a smoker. However, information such as other behavioraldeterminants of health may be missing, including information regardingalcohol use, exercise frequency, supplement use, meditation practices,yoga practices and the like. Computing device 104 transmits a request toa remote device 116 operated by the human subject to obtain moreinformation. Such a request may be transmitted utilizing any networkmethodology as described herein. Computing device 104 receives from theremote device 116 operated by the human subject a response containing atleast an element of information. Such information received from theremote device 116 operated by the human subject may be utilized togenerate classification algorithm. In an embodiment, such informationreceived from the remote device 116 may be stored within user database144.

With continued reference to FIG. 5, at step 515 a computing device 104generates a classification algorithm 132 utilizing a conditionalindicator 120. A classification algorithm 132 includes any of theclassification algorithm 132 as described above in more detail inreference to FIG. 1. In an embodiment, computing device 104 may select aclassification algorithm 132 utilizing input contained within expertdatabase 124. A classification algorithm 132 utilizes a conditionalindicator 120 as an input and outputs a conditional profile 136. Aconditional profile 136 includes any of the conditional profile 136 asdescribed above in more detail in reference to FIG. 1. A conditionalprofile 136 describes the overall health and/or well-being of a humansubject. For example, a conditional profile 136 may describe on asliding scale, the overall health and/or well-being of a human subject.For example, a conditional profile 136 may specify that a user is ingood physical health but needs to work on emotional health because theuser has very few friends and does not engage in many hobbies. In yetanother non-limiting example, a conditional profile 136 may describethat a user is in poor physical health as noted from conditionalindicators contained within a plurality of photographs 108 as the userappears to have puffy eyes, bloated cheeks, a pale demeanor, and looksto be exhausted. Computing device 104 is configured to retrieve anelement of user physiological data 140 that may provide informationutilized to generate a conditional profile 136. An element of userphysiological data 140 includes any of the elements of userphysiological data 140 as described above in more detail. For instanceand without limitation, an element of user physiological data 140 mayinclude a stool sample analyzed for one or more strains of bacteriainside a user's gut. In yet another non-limiting example, an element ofuser physiological data 140 may include a urine sample analyzed for oneor more nutrients such as iodine. Computing device 104 generates aclassification algorithm 132 utilizing an element of user physiologicaldata 140. An element of user physiological data 140 may be stored inuser database 144, as described above in more detail in reference toFIG. 1.

With continued reference to FIG. 5, at step 520, computing device 104determines a conditional status 148 of a human subject utilizing theconditional profile 136. A conditional status 148, includes any of theconditional status 148 es as described above in more detail in referenceto FIG. 1. A conditional status 148 identifies any health conditionsthat a user may be and/or is likely to be suffering from. A healthcondition includes any of the health conditions as described above inmore detail in reference to FIG. 1. Computing device 104 determines aconditional status 148 utilizing information contained within aconditional profile 136, indicating the overall health and/or well-beingof a user. For instance and without limitation, a conditional profile136 may indicate that a user has a very small social network, the userhas very few hobbies, the user sleeps a lot, and the user has lowVitamin D. In such an instance, computing device 104 may utilize theinformation contained within the user's conditional profile 136 todetermine a conditional status that indicates the user may be sufferingfrom depression. A conditional status 148 may be determined utilizinginput contained within expert database 124. Computing device 104generates a conditional status 148 that contains a disease likelihoodscore. A disease likelihood score includes any of the disease likelihoodscores as described above in more detail in reference to FIG. 1.Computing device 104 generates a conditional status 148 that contains atreatment identifier. A treatment identifier includes any of thetreatment identifies as described above in more detail in reference toFIG. 1. Computing device 104 determines a conditional status bycalculating a machine-learning process 152. A machine-learning process152 includes any of the machine-learning process 152 es as describedabove in more detail in reference to FIG. 1. A machine-learning process152 utilizes a conditional profile 136 as an input and outputs aconditional status 148. Computing device 104 determines the conditionalstatus 148 of a human subject utilizing a machine-learning process 152.

With continued reference to FIG. 5, computing device 104 transmits aconditional status 148 of a human subject to a remote device 116operated by an informed advisor. An informed advisor includes any of theinformed advisors as described above in more detail in reference toFIG. 1. A conditional status 148 may be transmitted from computingdevice 104 to a remote device 116 operated by an informed advisorutilizing any network methodology as described herein. Computing device104 receives an input generated by the informed advisor in response tothe conditional status 148 of the human subject. In an embodiment, aninput generated by the informed advisor may provide more informationabout the human subject, may confirm a treatment identified within theconditional status 148, and/or may confirm or deny a disease identifiedwithin a conditional status 148. Computing device 104 updates aconditional status utilizing input generated by an informed advisor.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An artificial intelligence system for analyzingimagery, the system comprising a computing device, the computing devicedesigned and configured to: receive a plurality of photographs relatedto a human subject; receive an element of user physiological data;analyze the plurality of photographs to identify a conditional indicatorcontained within the plurality of photographs; generate a classificationalgorithm, wherein the classification algorithm utilizes: theconditional indicator and the element of physiological data as inputs,wherein the element of physiological data is classified to a diseasestate category from a plurality of disease state categories; and outputsa conditional profile; and determine a conditional status of the humansubject utilizing the conditional profile.
 2. The system of claim 1,wherein the computing device is further configured to receive at animage capture device located on the computing device a wirelesstransmission from a remote device containing the plurality ofphotographs related to a human subject.
 3. The system of claim 1,wherein the computing device is further configured to receive theplurality of photographs related to the human subject from a socialnetworking platform.
 4. The system of claim 1, wherein the computingdevice is further configured to: identify a conditional indicator groupidentified within the plurality of photographs; and generate a label,identifying the conditional indicator group.
 5. The system of claim 4,wherein the computing device is further configured to: identifyinformation missing from a conditional indicator group; transmit arequest to a remote device operated by the human subject, to obtain moreinformation relating to the conditional indicator group; and receivefrom the remote device operated by the human subject a responsecontaining at least an element of information.
 6. The system of claim 1,wherein the computing device is further configured to: perform amachine-learning process, wherein the machine-learning process utilizesthe conditional profile as an input and outputs the conditional status;and determine the conditional status of the human subject utilizing themachine-learning process.
 7. The system of claim 1, wherein theconditional status further comprises a disease likelihood score.
 8. Thesystem of claim 1, wherein the conditional status further comprises atreatment identifier.
 9. The system of claim 1, wherein the computingdevice is further configured to: transmit the conditional status of thehuman subject to a remote device operated by an informed advisor;receive an input submitted to the remote device in response to theconditional status of the human subject; and update the conditionalstatus utilizing the input.
 10. An artificial intelligence method ofanalyzing imagery, the method comprising: receiving by a computingdevice, a plurality of photographs related to a human subject; receivingby the computing device, an element of user physiological data;analyzing by the computing device, the plurality of photographs toidentify a conditional indicator contained within the plurality ofphotographs; generating by the computing device, a classificationalgorithm utilizing the conditional indicator, wherein theclassification algorithm utilizes the conditional indicator and theelement of physiological data as inputs, wherein the element ofphysiological data is classified to a disease state category from aplurality of disease state categories; and outputs a conditionalprofile; and determining by the computing device, a conditional statusof the human subject utilizing the conditional profile.
 11. The methodof claim 10, wherein receiving the plurality of photographs furthercomprises receiving at an image capture device located on the computingdevice a wireless transmission from a remote device containing theplurality of photographs related to a human subject.
 12. The method ofclaim 10, wherein receiving the plurality of photographs related to thehuman subject further comprises receiving the plurality of photographsrelated to the human subject from a social networking platform.
 13. Themethod of claim 10, wherein analyzing the plurality of photographsfurther comprises: identifying a conditional indicator group identifiedwithin the plurality of photographs; and generating a label, identifyingthe conditional indicator group.
 14. The method of claim 13 furthercomprising: identifying information missing from a conditional indicatorgroup; transmitting a request to a remote device operated by the humansubject, to obtain more information relating to the conditionalindicator group; and receiving from the remote device operated by thehuman subject a response containing more information.
 15. The method ofclaim 10, wherein determining the conditional status further comprises:calculating a machine-learning process, wherein the machine-learningprocess utilizes the conditional profile as an input and outputs theconditional status; and determining the conditional status of the humansubject utilizing the machine-learning process.
 16. The method of claim10, wherein the conditional status further comprises a diseaselikelihood score.
 17. The method of claim 10, wherein the conditionalstatus further comprises a treatment identifier.
 18. The method of claim10 further comprising: transmitting the conditional status of the humansubject to a remote device operated by an informed advisor; receiving aninput generated by the informed advisor in response to the conditionalstatus of the human subject; and updating the conditional statusutilizing the input generated by the informed advisor.